Intelligent optimization control system and method for coke oven production process based on big data analysis

Abstract

The present application relates to the technical field of device process program control, and discloses a coke oven production process intelligent optimization control system and method based on big data analysis, the system comprises a coking temperature state monitoring module, a coking raw gas state monitoring module, a coke forming state monitoring module and a coking end state monitoring module; the coking oven management platform efficiently collects coking oven coke coal state images, combines intelligent image search algorithms with standard coke forming standard state images set by the standard, efficiently identifies the internal forming coke of the coking oven, intelligently and reliably monitors the coking end state based on a progressive detection chain formed by the coking temperature change trend of the coking oven, the coking raw gas release amount change trend and the forming coke, and improves the precision and quality of the coking production process control.

Description

Technical Field

[0001] This invention relates to the technical field of equipment process program control, specifically to an intelligent optimization control system and method for coke oven production process based on big data analysis. Background Technology

[0002] A coke oven consists of a carbonization chamber, combustion chamber, regenerator, inclined flue zone, roof, foundation, and flue. In the carbonization chamber, coal is heated and transformed into coke under air-isolated conditions. A single coke oven has dozens of alternating carbonization and combustion chambers, separated by refractory materials. Each combustion chamber has 20-30 vertical flues. Preheated gas and air from the regenerator meet and burn at the bottom of the vertical flues, providing heat to the carbonization chamber from the side. The regenerator is located at the bottom of the coke oven and uses high-temperature exhaust gas to preheat the gas and air used for heating. The inclined flue zone is an inclined passage connecting the regenerator and combustion chamber. The furnace body above the carbonization and combustion chambers is called the roof, and its thickness is determined according to the furnace body strength and the need to reduce the surface temperature of the roof. The roof zone has coal charging holes and riser holes leading to the carbonization chamber for charging coal and discharging raw coal gas generated during coal dry distillation. It also has observation holes leading to each flue for temperature measurement and flame inspection, allowing for adjustment of temperature and pressure based on the test results. The entire coke oven is built on a solid and flat concrete foundation. Each regenerator is connected to the flue via a waste gas conduit. The flue is located within or on both sides of the foundation, with one end connected to the chimney. The coking process in a coke oven requires a comprehensive assessment of the coking completion status by observing changes in solid coking coal and gaseous raw coal gas products. Currently, the coking production process in coke ovens cannot intelligently and comprehensively evaluate the coking completion status based on coking status information, nor can it efficiently and accurately control the end of coking in the coke oven.

[0003] Chinese invention patent application CN118605253A, published on September 6, 2024, discloses a safety monitoring system and method for the coking industry, used for detecting coking equipment and personnel. The system includes an equipment safety monitoring module, a production safety control module, a personnel detection control module, a limit detection module, and a central processing module. The equipment safety monitoring module monitors the operation of electrical and mechanical equipment throughout the entire coking process, from coking to quenching. The production safety control module interlocks all vehicles and operating equipment throughout the coking process, ensuring that the controlled vehicles and operating equipment start, stop, and shut down according to the process sequence. The personnel detection control module collects data on personnel and equipment in the work area, divides the work area, and monitors the distance between personnel and equipment within the work area. The central processing module receives detection results from each module and sends control or alarm commands to each module based on the output detection results, adjusting the start and stop of each module. However, the above technical solution cannot intelligently identify and accurately control the completion of coking in a coking oven. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the current issues of coking ovens' inability to intelligently and comprehensively assess the completion status of coking based on coking status information, and the inability to efficiently and accurately control the termination of coking operations, this paper aims to achieve the following objectives: accurate analysis of coking temperature change trends, efficient assessment of raw coking gas release trends, accurate detection of coke forming status, visual feedback of the coking oven's completion status, and intelligent termination of coking operations.

[0006] (II) Technical Solution

[0007] This invention is achieved through the following technical solution: an intelligent optimization control method for coke oven production process based on big data analysis, the method comprising the following steps:

[0008] S1. Acquire coking oven temperature over time;

[0009] S2. Based on the coking oven temperature-time image and the standard temperature-time image showing the stable change trend of coking temperature, perform analysis and processing on the change trend of coking temperature inside the coking oven over time to generate coking temperature change trend analysis data; when the state is unstable, continue to perform coking operation.

[0010] S3. When the state is stable, collect a time image of the release of raw coking coal gas.

[0011] S4. Based on the time image of the coking raw gas release and the time image of the standard raw gas release with stable release trend, evaluate the change trend of the coking raw gas release inside the coking oven over time, and generate coking raw gas release trend evaluation data; when it is in an unstable state, continue to perform coking operation.

[0012] S5. When the state is stable, collect images of the coking coal state in the coking oven;

[0013] S6. Based on the coking coal state image and the standard coke forming state image, perform coking forming state identification processing on the coking coal inside the coking oven to convert into coke, and generate coke forming state identification data; when it is coking coal, continue to perform coking operation.

[0014] S7. When the product is coke, construct the coking test result data of the coking oven and execute the coking oven coking end information feedback operation and the coking oven coking end operation step by step.

[0015] Preferably, the operation steps for acquiring the coking oven temperature-time image are as follows:

[0016] S11. The coking oven management platform collects the internal temperature parameters of the coking oven during any time period during the coking operation, forming a two-dimensional temperature-time curve, and generates a coking oven temperature-time image. The internal temperature change trend of the coking oven during the coking operation includes the temperature rise trend and the temperature level stability trend.

[0017] Preferably, the coking temperature over time is analyzed based on the coking oven temperature-time image and the standard temperature-time image showing a stable coking temperature change trend, generating coking temperature change trend analysis data; when the temperature is unstable, the following steps are performed to continue the coking operation:

[0018] S21. Establish a standard temperature-time image set for the stable change trend of coking temperature. , ;in Indicates the first A standard temperature-time graph showing the steady trend of coking temperature changes. The maximum number of standard temperature-time images representing the stable trend of coking temperature; the standard temperature-time image representing the stable trend of coking temperature represents a two-dimensional temperature-time curve formed when the internal temperature parameters of the coking oven tend to be in a horizontal and stable state after the coking coal is completely converted into coke during the coking process.

[0019] S22. Combine the coking oven temperature-time image with the standard temperature-time image set showing the stable change trend of coking temperature. The standard temperature-time graph showing the steady change trend of coking temperature described in the figure Image feature matching is performed, and coking temperature change trend analysis data is generated based on the image feature matching results. The specific steps for generating the coking temperature change trend analysis data are as follows:

[0020] S221. Initialization: Define the relevant structural parameters as vectors, and set the standard temperature-time image set of the stable change trend of coking temperature. The search space contains all the standard temperature-time images showing the steady trend of coking temperature changes. Defined as The solution to the dimensional optimization problem, the coking state search sand cat, represents the 1× solution to the problem. Array, each variable value They are all floating-point numbers, each variable value to It is a standard temperature-time image set located at the stable change trend of the coking temperature. Between the lower and upper bounds of the search space, the fitness of each coking state search sand cat is obtained through the fitness function of the problem to be solved;

[0021] S222, Searching for prey, the final and main parameters controlling the transition between the exploration and development phases are: ,when When >1, the coking state search sandcat is in the standard temperature time image set of the coking temperature stable change trend. Search the search space for a standard temperature-time image showing the stable trend of coking temperature that matches the coking oven temperature-time image. The search process for a sand cat in the coking state relies on the release of low-frequency noise. Assuming the sensitivity range of the sand cat search in the coking state... From 0 to 2kHz, This represents the auditory factor for searching a sand cat in a coking state, assumed to have a value of 2. This represents the current iteration number. The maximum number of iterations is given by `rand(0,1)`, which represents a random number between 0 and 1. , ,in Represents the sensitivity vector; each coking state search sand cat is based on the best candidate position. and current location and its sensitivity range Update its position, that is, in the standard temperature time image set of the stable change trend of coking temperature. Search the search space for the standard temperature-time image of the stable change trend of the coking temperature that best matches the coking oven temperature-time image. The location; the formula for calculating the location is as follows: ,in This represents the search for a sand cat individual in the coking state during iteration t+1, within the standard temperature-time image set representing the stable trend of coking temperature changes. The current position in the search space. express The next iteration of the coking state search for the sand cat in the standard temperature time image set of the coking temperature stable change trend. The best candidate position in the search space;

[0022] S223, Attack prey, when When ≤1, the coking state search sandcat is in the standard temperature time image set of the coking temperature stable change trend. Search within the search space, utilizing the best candidate position. With current location Generate a random location, namely, the standard temperature time image set of the stable change trend of coking temperature. Randomly search the search space for a standard temperature-time image that matches the stable temperature change trend of the coking oven. The random location is assumed to be a circle for the sensitivity range of the search for sand cats in the coking state. A roulette wheel method is used to randomly select a corner for each search sand cat in the coking state. The formula for calculating the random position is as follows: The standard temperature-time image set of the stable change trend of coking temperature was used. The search, among which Represents the sensitivity vector. Indicates angle The cosine value, This indicates the search for sand cats in the coking state, representing the stable temperature change trend of the coking temperature in the standard temperature time image set. The random location in the search space allows the sand cat in the coking state to approach and attack its prey, that is, in the standard temperature time image set of the stable change trend of the coking temperature. Search the search space for a standard temperature-time image showing the stable trend of coking temperature that matches the coking oven temperature-time image. ;

[0023] S224. When the search algorithm reaches its maximum number of iterations, it outputs a standard temperature-time image of the stable change trend of the coking temperature that matches the coking oven temperature-time image. Otherwise, continue iterating until the maximum number of iterations is met;

[0024] S225. Based on the coking oven temperature-time image and the standard temperature-time image showing the stable change trend of coking temperature output in step S224. The image feature matching results are used to generate coking temperature change trend analysis data;

[0025] When the coking oven temperature-time image and If image feature matching is successful, it indicates that the temperature change trend inside the coking oven over time is approaching a stable state. Once the coking operation is completed, the output coking temperature change trend analysis data is in a stable state.

[0026] When the coking oven temperature-time image and If no matching of image features is found, it indicates that the current temperature change trend inside the coking oven over time is not trending towards a stable state, and the coking operation is not completed. Therefore, the output of the coking temperature change trend analysis data is "unstable state"; the coking operation continues.

[0027] Preferably, when the condition is stable, the operation steps for acquiring time-varying images of raw coking coal gas release are as follows:

[0028] S31. When the coking temperature change trend analysis data is in an unstable state, the coking oven management platform collects the raw coal gas release parameters of the coking oven during any time period during the coking operation, forming a two-dimensional curve of raw coal gas release amount versus time, and generates a coking raw coal gas release amount time image. The raw coal gas contains hydrogen and methane. The change trend of the raw coal gas release amount during the coking operation includes an upward trend of raw coal gas release amount and a stable trend of raw coal gas release amount.

[0029] Preferably, the change trend of coking gas release over time is evaluated based on the time-varying image of raw coking gas release and the time-varying image of raw coking gas release with a stable release trend, generating raw coking gas release trend evaluation data; when the state is unstable, the following steps are followed to continue the coking operation:

[0030] S41. Establish a time-based image set of standard raw coal gas release rates to indicate a stable release trend in coking coal gas. , ,in Indicates the first A time-varying graph showing the steady release trend of standard raw coal gas from coking coal. The maximum value of the number of standard raw gas release time images representing the stable release trend of coking raw gas; the standard raw gas release time image representing the stable release trend of coking raw gas represents a two-dimensional curve of raw gas release amount versus time formed when the raw gas release amount parameter inside the coking oven changes with the time parameter after the coking coal is completely converted into coke during the coking process.

[0031] S42. The FLANN image search algorithm is used to compare the time-series images of the coking coal gas release with the time-series images of the standard coking coal gas release with the stable release trend. The image shows the stable release trend of raw coking coal gas and the time-varying amount of raw coal gas. Perform image feature matching, and generate coking coal gas release trend assessment data based on the image feature matching results;

[0032] The time-varying image of the coking coal gas release. If image feature matching is successful, it means that the current trend of raw coal gas release from the coking oven over time is relatively stable. Once the coking operation is completed, the raw coal gas release trend assessment data is output as stable.

[0033] The time-varying image of the coking coal gas release. If no matching of image features is found, it indicates that the current trend of raw gas release from the coking oven over time is not in a stable state, and the coking operation is not completed. Therefore, the raw gas release trend assessment data is output as unstable, and the coking operation continues.

[0034] Preferably, when the condition is stable, the operation steps for acquiring the coking coal condition image of the coking oven are as follows:

[0035] S51. When the coking coal gas release trend assessment data is in an unstable state, the coking oven management platform collects real-time image information of the coking coal inside the coking oven during the coking operation, and generates a coking coal status image set. , in Indicates the number of collections Image of the coking coal condition in a coking oven. This represents the maximum number of images of the coking coal condition collected from the coking oven.

[0036] Preferably, the process of identifying the coking state of coking coal inside the coking oven as it is converted into coke is performed based on the image of the coking coal state and the standard image of coke forming, generating coke forming state identification data; when it is coking coal, the following steps are performed to continue the coking operation:

[0037] S61. Establish a standard state image set for coke forming. , ;in Indicates the first A standard image of coke forming. This represents the maximum number of standard state images of coke forming; the standard state images of coke forming represent the standard appearance image information of the formed coke inside the coking oven after the coking process of coking coal is completely converted into coke. The coking coal appears dark blackish-brown, and the coke appears silver-gray.

[0038] S62. The SURF image search algorithm is used to compare the coking coal state image of the coke oven with the standard state image set of coke forming. The image shows the standard state of coke forming. Perform image feature matching and generate coke forming state recognition data based on the image feature matching results;

[0039] When the image of the coking coal state in the coke oven is... If image feature matching is successful, it means that the coking coal inside the coking oven has been converted into shaped coke. Then, the coke forming status identification data is output as coke.

[0040] When the image of the coking coal state in the coke oven is... If no matching of image features is found, it indicates that the coking coal inside the coking oven has not yet been converted into shaped coke. In this case, the coke forming status identification data is output as coking coal, and the coking operation continues.

[0041] Preferably, when the product is coke, the operation steps for constructing coke oven coking test result data and performing coke oven coking completion information feedback and coke oven coking completion operations in stages are as follows:

[0042] S71. When the coke forming state identification data is coke, the coking temperature change trend analysis data, the coking raw gas release trend assessment data, and the coke forming state identification data are combined and identified to construct the coking oven coking test result data and pushed back to the coking oven management platform through the Internet of Things communication network.

[0043] S72. The coking oven management platform receives the coking oven coking test result data and controls the coking oven to end the coking operation.

[0044] The coke oven production process intelligent optimization control system based on big data analysis is used to implement the coke oven production process intelligent optimization control method based on big data analysis. The system includes a coking temperature status monitoring module, a coking raw gas status monitoring module, a coke forming status monitoring module, and a coking end status monitoring module.

[0045] The coking temperature status monitoring module includes a coking oven temperature time image acquisition unit, a standard temperature time image storage unit for stable coking temperature change trend, and a coking temperature change trend analysis unit.

[0046] The coking oven temperature and time image acquisition unit acquires coking oven temperature and time images through the coking oven management platform; the coking temperature stable change trend standard temperature and time image storage unit stores the coking temperature stable change trend standard temperature and time image; the coking temperature change trend analysis unit performs coking oven temperature and time image and coking temperature stable change trend standard temperature and time image analysis on the coking oven temperature and time image and generates coking temperature change trend analysis data.

[0047] The coking gas status monitoring module includes a coking gas release time image acquisition unit, a coking gas release time image storage unit for stable release trend standard coking gas release, and a coking gas release trend assessment unit.

[0048] The coking oven raw gas release time image acquisition unit acquires coking oven raw gas release time images through the coking oven management platform; the coking oven raw gas stable release trend standard raw gas release time image storage unit is used to store the coking oven raw gas stable release trend standard raw gas release time images; the coking oven raw gas release trend assessment unit evaluates the change trend of coking oven raw gas release over time based on the coking oven raw gas release time images and the coking oven raw gas stable release trend standard raw gas release time images, and generates coking oven raw gas release trend assessment data.

[0049] The coke forming state monitoring module includes a coking coal state image acquisition unit, a coke forming standard state image storage unit, and a coke forming state recognition unit.

[0050] The coking coal state image acquisition unit acquires coking coal state images through the coking oven management platform; the coke forming standard state image storage unit stores coke forming standard state images; and the coke forming state recognition unit performs coking forming state recognition processing on the coking coal state images and coke forming standard state images inside the coking oven to generate coke forming state recognition data.

[0051] The coking end status monitoring module includes a coking end status feedback unit and a coking end control unit.

[0052] The coking end status feedback unit, based on the coking temperature change trend analysis information, the coking raw gas release trend assessment information, and the coke forming status identification information, combines data processing to construct coking oven coking detection result data and feeds it back to the coking oven management platform online; the coking end control unit, the coking oven management platform receives the coking oven coking detection result data and controls the coking oven to end the coking operation.

[0053] (III) Beneficial Effects

[0054] This invention provides an intelligent optimization control system and method for coke oven production processes based on big data analysis. It has the following beneficial effects:

[0055] I. By dynamically acquiring coking oven temperature and time image information through the coking oven management platform, we provide real data support for accurately monitoring the end status of coking in the coking oven; based on the coking oven temperature and time image, combined with artificial intelligence algorithms and standard temperature and time images of stable coking temperature changes based on big data storage, we perform real-time and efficient analysis of the coking temperature change trend inside the coking oven over time, enabling accurate identification of the end status of coking based on the coking temperature change trend, and improving the scientific nature of coking production process control.

[0056] Second, by dynamically acquiring time-varying images of raw coking gas release through the coking oven management platform, reliable data support is provided for accurately monitoring the end-of-coking status of the coking oven. Based on these images, combined with intelligent image search algorithms and standard raw coking gas release time-varying images set based on big data to assess the time-varying trend of raw coking gas release within the coking oven, an intelligent assessment of the time-varying trend of raw coking gas release is achieved. This enables accurate identification of the end-of-coking status through a detection chain based on the trends of coking oven temperature changes and raw coking gas release. Furthermore, by efficiently acquiring images of coking coal status through the coking oven management platform and combining them with intelligent image search algorithms and standard coke forming status images, efficient identification of formed coke within the coking oven is achieved. This enables intelligent and reliable monitoring of the end-of-coking status through a progressive detection chain based on the trends of coking oven temperature changes, raw coking gas release changes, and formed coke, thereby improving the accuracy and quality of coking production process control.

[0057] Third, by combining data processing with information on coking temperature change trends, raw coking gas release trends, and coke forming status identification, coking oven coking detection information is efficiently constructed and promptly and efficiently fed back online to the coking oven management platform, realizing efficient and visualized feedback of coking production process information; the coking oven management platform receives the coking oven coking detection results data and autonomously controls the coking oven to end coking operations, improving the safety and intelligence of coking production process control. Attached Figure Description

[0058] Figure 1 A schematic diagram of the modules of the intelligent optimization control system for coke oven production process based on big data analysis provided by the present invention;

[0059] Figure 2 The flowchart shows the intelligent optimization control method for coke oven production process based on big data analysis provided by this invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] An example of the intelligent optimization control system and method for coke oven production process based on big data analysis is as follows: Example

[0062] Please see Figure 1 - Figure 2A smart optimization control method for coke oven production process based on big data analysis, the method includes the following steps:

[0063] S1. Acquire coking oven temperature over time;

[0064] S2. Based on the coking oven temperature-time image and the standard temperature-time image showing the stable change trend of coking temperature, analyze and process the change trend of coking temperature inside the coking oven over time to generate coking temperature change trend analysis data; when the state is unstable, continue to execute the coking operation.

[0065] S3. When the state is stable, collect a time image of the release of raw coking coal gas.

[0066] S4. Based on the time-varying image of raw coking gas release and the time-varying image of raw coking gas release with stable release trend, evaluate the change trend of raw coking gas release inside the coking oven over time, and generate raw coking gas release trend evaluation data; when it is in an unstable state, continue to perform coking operations.

[0067] S5. When the state is stable, collect images of the coking coal state in the coking oven;

[0068] S6. Based on the image of the coking coal state in the coking oven and the standard image of coke forming state, identify the coking forming state of the coking coal inside the coking oven as it is converted into coke, and generate coke forming state identification data; when it is coking coal, continue to perform coking operation.

[0069] S7. When the product is coke, construct the coking test result data of the coking oven and execute the coking oven coking end information feedback operation and the coking oven coking end operation step by step.

[0070] For further details, please refer to Figure 1 - Figure 2 The steps for acquiring temperature and time images of a coking oven are as follows:

[0071] S11. The coking oven management platform collects the internal temperature parameters of the coking oven during any time period during the coking operation, forming a two-dimensional temperature-time curve, and generates a coking oven temperature-time image. The internal temperature change trend of the coking oven during the coking operation includes the temperature rise trend and the temperature level stability trend.

[0072] Based on the coking oven temperature-time image and the standard temperature-time image showing a stable coking temperature change trend, the coking temperature inside the coking oven is analyzed over time to generate coking temperature change trend analysis data. When the temperature is not stable, the following operating steps continue to be performed for coking operations:

[0073] S21. Establish a standard temperature-time image set for the stable change trend of coking temperature. , ,in Indicates the first A standard temperature-time graph showing the steady trend of coking temperature changes. The maximum number of standard temperature-time images representing the stable trend of coking temperature; the standard temperature-time image representing the stable trend of coking temperature represents a two-dimensional temperature-time curve formed when the internal temperature parameters of the coking oven tend to be in a horizontal and stable state after the coking coal is completely converted into coke during the coking process.

[0074] S22. Compare the coking oven temperature-time image with the standard temperature-time image set showing the stable trend of coking temperature. Standard temperature-time graph showing the steady trend of coking temperature. Perform image feature matching, and generate coking temperature change trend analysis data based on the image feature matching results; the specific steps for generating coking temperature change trend analysis data are as follows:

[0075] S221. Initialization: Define the relevant structural parameters as vectors, and set the standard temperature-time image set of the stable change trend of coking temperature. Standard temperature-time graphs showing the steady-state trend of all coking temperatures in the search space. Defined as The solution to the dimensional optimization problem, the coking state search sand cat, represents the 1× solution to the problem. Array, each variable value They are all floating-point numbers, each variable value to It is a standard temperature-time image set located at the steady change trend of coking temperature. Between the lower and upper bounds of the search space, the fitness of each coking state search sand cat is obtained through the fitness function of the problem to be solved;

[0076] S222, Searching for prey, the final and main parameters controlling the transition between the exploration and development phases are: ,when When >1, the coking state search sand cat is in the standard temperature time image set of the coking temperature stable change trend. Search the search space for standard temperature-time images that match the steady trend of coking temperature changes in the coking oven. The search process for a sand cat in the coking state relies on the release of low-frequency noise. Assuming the sensitivity range of the sand cat search in the coking state... From 0 to 2kHz, This represents the auditory factor for searching a sand cat in a coking state, assumed to have a value of 2. This represents the current iteration number. The maximum number of iterations is given by `rand(0,1)`, which represents a random number between 0 and 1. , ,in Represents the sensitivity vector; each coking state search sand cat is based on the best candidate position. and current location and its sensitivity range Update its position, i.e., within the standard temperature time image set showing the stable trend of coking temperature changes. Search the search space for the standard temperature-time image that best matches the steady trend of coking temperature change in the coking oven temperature-time image. The location; the formula for calculating the location is as follows: ,in This represents the standard temperature-time image set showing the stable trend of coking temperature for individual sand cats during the t+1 iteration of the coking state search. The current position in the search space. express The next iteration of the coking state search uses a sand cat to analyze the stable trend of coking temperature changes within a standard temperature-time image set. The best candidate position in the search space;

[0077] S223, Attack prey, when When ≤1, the coking state search sand cat is in the standard temperature time image set of the coking temperature stable change trend. Search within the search space, utilizing the best candidate position. With current location Generate a random location, i.e., a standard temperature time image set showing a stable trend in coking temperature. Randomly search the search space for standard temperature-time images that match the steady trend of coking temperature changes in the coking oven. The random location is assumed to be a circle for the sensitivity range of the search for sand cats in the coking state. A roulette wheel method is used to randomly select a corner for each search sand cat in the coking state. The formula for calculating the random position is as follows: A standard temperature-time image set was used to analyze the stable change trend of coking temperature. The search, among which Represents the sensitivity vector. Indicates angle The cosine value, This indicates the search for sand cats in the coking state, showing the stable trend of coking temperature changes within a standard temperature-time image set. The random position in the search space allows the sand cat in the coking state to approach and attack its prey, i.e., in the standard temperature time image set with a stable trend of coking temperature change. Search the search space for standard temperature-time images that match the steady trend of coking temperature changes in the coking oven. ;

[0078] S224. When the search algorithm reaches the maximum number of iterations, output a standard temperature-time image showing the stable trend of coking temperature that matches the coking oven temperature-time image. Otherwise, continue iterating until the maximum number of iterations is met;

[0079] S225. Based on the coking oven temperature-time image and the standard temperature-time image showing the stable change trend of coking temperature output in step S224. The image feature matching results are used to generate coking temperature change trend analysis data;

[0080] When the coking oven temperature time graph and Successful image feature matching indicates that the temperature change trend inside the coking oven over time is approaching a stable state. Once the coking operation is completed, the output coking temperature change trend analysis data will be in a stable state.

[0081] When the coking oven temperature time graph and If no image features are successfully matched, it indicates that the current temperature change trend inside the coking oven over time is not trending towards a stable state, and the coking operation is not completed. Therefore, the output coking temperature change trend analysis data is in an unstable state; continue to execute the coking operation.

[0082] The coking oven temperature and time image acquisition unit dynamically acquires coking oven temperature and time image information through the coking oven management platform, providing real data support for accurate monitoring of the coking end status. The coking temperature stable change trend standard temperature and time image storage unit and the coking temperature change trend analysis unit work together to perform real-time and efficient analysis of the coking temperature change trend inside the coking oven over time based on the coking oven temperature and time images combined with artificial intelligence algorithms and the coking temperature stable change trend standard temperature and time images based on big data storage. This enables accurate identification of the coking end status based on the coking temperature change trend, improving the scientific nature of coking production process control.

[0083] For further details, please refer to Figure 1 - Figure 2 When the situation is stable, the steps for collecting time-varying images of raw coking coal gas release are as follows:

[0084] S31. When the coking temperature change trend analysis data is in an unstable state, the coking oven management platform collects the raw coal gas release parameters of the coking oven during any time period during the coking operation, forming a two-dimensional curve of raw coal gas release amount versus time, and generates a coking raw coal gas release amount over time image. The raw coal gas contains hydrogen and methane. The trend of raw coal gas release amount over time during the coking operation includes an upward trend and a stable trend.

[0085] Based on the time-varying image of raw coking gas release and the time-varying image of standard raw coking gas release with a stable release trend, the trend of raw coking gas release inside the coking oven over time is evaluated, generating raw coking gas release trend evaluation data. When the state is unstable, the following operating steps continue to be performed for coking operations:

[0086] S41. Establish a time-based image set of standard raw coal gas release rates to indicate a stable release trend in coking coal gas. , ;in Indicates the first A time-varying graph showing the steady release trend of standard raw coal gas from coking coal. The maximum number of standard raw gas release time images representing the stable release trend of coking raw gas; the standard raw gas release time image representing the stable release trend of coking raw gas represents a two-dimensional curve of raw gas release amount versus time formed when the raw gas release amount parameter inside the coking oven changes with the time parameter after the coking coal is completely converted into coke during the coking process.

[0087] S42. The FLANN image search algorithm is used to combine the time-series images of raw coking coal gas release with the standard raw coal gas release time-series image set showing a stable release trend. Stable release trend of raw coking coal gas in China; standard raw coal gas release volume over time (image). Perform image feature matching, and generate coking coal gas release trend assessment data based on the image feature matching results;

[0088] Time graph of coking coal gas release Successful image feature matching indicates that the current trend of raw coal gas release from the coking oven over time is relatively stable. Once the coking operation is completed, the output raw coal gas release trend assessment data will be stable.

[0089] Time graph of coking coal gas release If no image feature matching is successful, it indicates that the current trend of raw gas release from the coking oven is not in a stable state over time, and the coking operation is not completed. Therefore, the output raw gas release trend assessment data is in an unstable state; continue to execute the coking operation.

[0090] When the coking coal is in a stable state, the operation steps for acquiring the coking coal state image of the coking oven are as follows:

[0091] S51. When the assessment data of the release trend of raw coking coal gas is unstable, the real-time status image information of coking coal inside the coking oven during the coking operation is collected online through the coking oven management platform, and a coking coal status image set is generated. , ,in Indicates the number of collections Image of the coking coal condition in a coking oven. This represents the maximum number of images of the coking coal condition collected from the coking oven.

[0092] Based on the images of coking coal state and standard coke forming state, the coking coal state inside the coking oven is identified and processed to generate coke forming state identification data. When it is coking coal, the following coking operation steps are performed:

[0093] S61. Establish a standard state image set for coke forming. , ,in Indicates the first A standard image of coke forming. This represents the maximum number of standard state images of coke forming; the standard state image of coke forming represents the standard appearance image information of the formed coke inside the coking oven after the coking process of coking coal is completely converted into coke. The coking coal appears dark blackish-brown, and the coke appears silver-gray.

[0094] S62. The SURF image search algorithm is used to combine the coking coal state images of the coking oven with the standard state images of coke forming. Standard state image of medium coke forming Perform image feature matching and generate coke forming state recognition data based on the image feature matching results;

[0095] When the coking coal state image in the coking oven is compared with If image feature matching is successful, it means that the coking coal inside the coke oven has been converted into shaped coke. The output coke forming status identification data is then coke.

[0096] When the coking coal state image in the coking oven is compared with If no image feature matching is successful, it indicates that the coking coal inside the coking oven has not yet been converted into shaped coke. In this case, the output coke forming status identification data is coking coal; continue the coking operation.

[0097] The time-based image acquisition unit for raw coking gas release from the coking oven utilizes the coking oven management platform to dynamically acquire such images, providing reliable data support for accurately monitoring the end-of-coking status. The storage unit for standard raw coking gas release images reflecting a stable release trend and the evaluation unit work together to intelligently assess the time-varying trend of raw coking gas release within the coking oven. This is achieved by combining the raw coking gas release time images with an intelligent image search algorithm and the standard raw coking gas release time images based on big data analysis, thus realizing a coking oven-based intelligent assessment of the time-varying trend of raw coking gas release. The detection chain, consisting of the trends in coking temperature and raw coking gas release, accurately identifies the end-of-coking state. The coking coal state image acquisition unit and the coke forming state recognition unit work together to efficiently acquire images of the coking coal state through the coking oven management platform. Combined with intelligent image search algorithms and standard coke forming state images, this enables efficient identification of formed coke inside the coking oven. This achieves intelligent and reliable monitoring of the end-of-coking state based on a progressive detection chain consisting of trends in coking temperature, raw coking gas release, and formed coke, thereby improving the accuracy and quality of coking production process control.

[0098] For further details, please refer to Figure 1 - Figure 2 When the product is coke, the operation steps for constructing coking oven coking test result data and executing the coking oven coking completion information feedback and coking oven coking completion operations in stages are as follows:

[0099] S71. When the coke forming state identification data is coke, the coking temperature change trend analysis data, the coking raw gas release trend assessment data, and the coke forming state identification data are combined and identified to construct the coking oven coking test result data and push it back to the coking oven management platform through the Internet of Things communication network.

[0100] S72. The coking oven management platform receives the coking test result data of the coking oven and controls the coking oven to end the coking operation.

[0101] Through the coking end status feedback unit, coking oven coking detection information is efficiently constructed based on coking temperature change trend analysis information, coking raw gas release trend assessment information, and coke forming status identification information, combined with data processing. This information is then fed back to the coking oven management platform in a timely and efficient manner, achieving efficient and visualized feedback of coking production process information. The coking end control unit receives the coking oven coking detection result data and autonomously controls the coking oven to end the coking operation, improving the safety and intelligence of coking production process control.

[0102] Example 2:

[0103] Please see Figure 1 - Figure 2 The coke oven production process intelligent optimization control system based on big data analysis is used to realize the intelligent optimization control method of coke oven production process based on big data analysis. The system includes a coking temperature status monitoring module, a coking raw gas status monitoring module, a coke forming status monitoring module, and a coking end status monitoring module.

[0104] The coking temperature status monitoring module includes a coking oven temperature time image acquisition unit, a standard temperature time image storage unit for stable coking temperature change trend, and a coking temperature change trend analysis unit.

[0105] The coking oven temperature and time image acquisition unit acquires coking oven temperature and time images through the coking oven management platform; the coking temperature stable change trend standard temperature and time image storage unit stores the coking temperature stable change trend standard temperature and time images; the coking temperature change trend analysis unit analyzes the change trend of coking temperature inside the coking oven over time based on the coking oven temperature and time images and the coking temperature stable change trend standard temperature and time images, and generates coking temperature change trend analysis data.

[0106] The coking gas status monitoring module includes a coking oven raw gas release time image acquisition unit, a coking oven raw gas stable release trend standard raw gas release time image storage unit, and a coking oven raw gas release trend assessment unit.

[0107] The coking oven raw gas release time image acquisition unit acquires coking oven raw gas release time images through the coking oven management platform; the coking oven raw gas stable release trend standard raw gas release time image storage unit stores the coking oven raw gas stable release trend standard raw gas release time images; the coking oven raw gas release trend assessment unit evaluates the change trend of coking oven raw gas release over time based on the coking oven raw gas release time images and the coking oven raw gas stable release trend standard raw gas release time images, and generates coking oven raw gas release trend assessment data.

[0108] The coke forming condition monitoring module includes a coking coal condition image acquisition unit, a coke forming standard condition image storage unit, and a coke forming condition recognition unit.

[0109] The coking coal state image acquisition unit acquires images of the coking coal state in the coking oven through the coking oven management platform; the coke forming standard state image storage unit stores images of the coke forming standard state; and the coke forming state recognition unit performs coking forming state recognition processing on the images of the coking coal state in the coking oven and the coke forming standard state images, generating coke forming state recognition data.

[0110] The coking end status monitoring module includes a coking end status feedback unit and a coking end control unit;

[0111] The coking end status feedback unit, based on the analysis information of coking temperature change trend, the assessment information of coking raw gas release trend, and the identification information of coke forming status, combines data processing to construct the coking oven coking detection result data and feeds it back to the coking oven management platform online; the coking end control unit, the coking oven management platform receives the coking oven coking detection result data and controls the coking oven to end the coking operation.

[0112] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent optimization control of coke oven production process based on big data analysis, characterized in that, The method includes the following steps: S1. Acquire coking oven temperature over time; S2. Based on the coking oven temperature-time image and the standard temperature-time image showing the stable change trend of coking temperature, perform analysis and processing on the change trend of coking temperature inside the coking oven over time to generate coking temperature change trend analysis data; when the state is unstable, continue to perform coking operation. S3. When the state is stable, collect a time image of the release of raw coking coal gas. S4. Based on the time image of the coking raw gas release and the time image of the standard raw gas release with stable release trend, evaluate the change trend of the coking raw gas release inside the coking oven over time, and generate coking raw gas release trend evaluation data; when it is in an unstable state, continue to perform coking operation. S5. When the state is stable, collect images of the coking coal state in the coking oven; S6. Based on the coking coal state image and the standard coke forming state image, perform coking forming state identification processing on the coking coal inside the coking oven to convert into coke, and generate coke forming state identification data; when it is coking coal, continue to perform coking operation. S7. When the product is coke, construct the coking test result data of the coking oven and execute the coking oven coking end information feedback operation and the coking oven coking end operation step by step.

2. The intelligent optimization control method for coke oven production process based on big data analysis according to claim 1, characterized in that: S1 includes the following steps: S11. The coking oven management platform is used to collect the temperature-time two-dimensional curve of the internal temperature parameters of the coking oven during any time period in the coking operation process, and generate a coking oven temperature-time image.

3. The intelligent optimization control method for coke oven production process based on big data analysis according to claim 2, characterized in that: S2 includes the following steps: S21. Establish a standard temperature-time image set for the stable change trend of coking temperature. The include ;in Indicates the first A standard temperature-time graph showing the steady trend of coking temperature changes. S22, Compare the coking oven temperature-time image with the... The above Image feature matching is performed, and coking temperature change trend analysis data is generated based on the image feature matching results. The specific steps for generating the coking temperature change trend analysis data are as follows: S221. Initialization: Define the relevant structure parameters as vectors, and... All of the above in the search space Defined as The solution to the dimensional optimization problem, the coking state search sand cat, represents the 1× solution to the problem. Array, each variable value They are all floating-point numbers, each variable value to It is located in the Between the lower and upper bounds of the search space, the fitness of each coking state search sand cat is obtained through the fitness function of the problem to be solved; S222, Searching for prey, the final and main parameters controlling the transition between the exploration and development phases are: ,when When >1, the coking state search sand cat is described in the following Search the search space to find the image that matches the temperature and time of the coking oven. Each coking-state search sand cat is based on the best candidate location. and current location and its sensitivity range Update your own location, that is, in the above Search the search space for the element that best matches the temperature-time image of the coking oven. Location; S223, Attack prey, when When ≤1, the coking state search sand cat is described in the above. Search within the search space, utilizing the best candidate position. With current location Generate a random location, that is, in the Randomly search the search space to find the image that matches the temperature and time of the coking oven. The random location is assumed to be a circle for the sensitivity range of the search for sand cats in the coking state. A roulette wheel method is used to randomly select a corner for each search sand cat in the coking state. ; S224. When the search algorithm reaches the maximum number of iterations, output the image that matches the coking oven temperature-time graph. Otherwise, continue iterating until the maximum number of iterations is met; S225. Based on the coking oven temperature-time image output in step S224 and the... The image feature matching results are used to generate coking temperature change trend analysis data; When the coking oven temperature-time image and If image feature matching is successful, the output coking temperature change trend analysis data is in a stable state. When the coking oven temperature-time image and If no matching of image features is found, the output of the coking temperature change trend analysis data is in an unstable state; the coking operation continues.

4. The intelligent optimization control method for coke oven production process based on big data analysis according to claim 3, characterized in that: S3 includes the following steps: S31. When the coking temperature change trend analysis data is in an unstable state, the coking oven management platform is used to collect the raw gas release parameters inside the coking oven during any time period in the coking operation process to form a two-dimensional curve of raw gas release amount versus time, and generate a coking raw gas release amount time image.

5. The intelligent optimization control method for coke oven production process based on big data analysis according to claim 4, characterized in that: S4 includes the following steps: S41. Establish a time-based image set of standard raw coal gas release volume to indicate a stable release trend in coking coal gas. The include ;in Indicates the first A time-varying graph showing the steady release trend of raw coking coal gas and the release volume of standard raw coal gas. S42. The FLANN image search algorithm is used to compare the time image of the coking coal gas release with the image of the coking coal gas release. The above Perform image feature matching and generate coking coal gas release trend assessment data based on the image feature matching results; The time-varying image of the coking coal gas release. If image feature matching is successful and the coking operation is completed, the output coking waste gas release trend assessment data is in a stable state. The time-varying image of the coking coal gas release. If no matching of image features is found, the output of the coking coal gas release trend assessment data is "unstable"; the coking operation continues.

6. The intelligent optimization control method for coke oven production process based on big data analysis according to claim 5, characterized in that: S5 includes the following steps: S51. When the coking coal gas release trend assessment data is in an unstable state, the coking oven management platform collects real-time image information of the coking coal inside the coking oven during the coking operation, and generates a coking coal status image set. The include ;in Indicates the number of collections Image showing the state of coking coal in a coking oven.

7. The intelligent optimization control method for coke oven production process based on big data analysis according to claim 6, characterized in that: S6 includes the following steps: S61. Establish a standard state image set for coke forming. The include ;in Indicates the first A standard image of coke forming; S62. The SURF image search algorithm is used to compare the coking coal state image of the coking oven with the image of the coking coal state of the coking oven. The above Perform image feature matching and generate coke forming status recognition data based on the image feature matching results; When the image of the coking coal state in the coke oven is... If image feature matching is successful, the coke forming state identification data is output as coke. When the image of the coking coal state in the coke oven is... If no matching of image features is found, the coke forming state identification data is output as coking coal; the coking operation continues.

8. The intelligent optimization control method for coke oven production process based on big data analysis according to claim 7, characterized in that: S7 includes the following steps: S71. When the coke forming state identification data is coke, the coking temperature change trend analysis data, the coking raw gas release trend assessment data, and the coke forming state identification data are combined and identified to construct the coking oven coking test result data and pushed back to the coking oven management platform through the Internet of Things communication network. S72. The coking oven management platform receives the coking oven coking test result data and controls the coking oven to end the coking operation.

9. A smart optimization control system for coke oven production process based on big data analysis, used to implement the smart optimization control method for coke oven production process based on big data analysis as described in any one of claims 1-8, characterized in that: The system includes a coking temperature status monitoring module, a coking raw gas status monitoring module, a coke forming status monitoring module, and a coking end status monitoring module.

Images